Early Classification of Time Series.
Early Classification of Time Series (ECTS) is a machine learning problem: correctly predict the label of a streaming time series using as few observations as possible [1]}.
Solutions choose one early timestep per time series at which the whole instance is classified (without cheating and looking at future values).
Classifying sequences early is classically targeted at time series
[2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}.
Most recent approaches [9]}, [10]}, [11]}, [12]}, [13]} have turned to deep learning, extending beyond traditional methods for univariate time series [14]}, [4]}, [16]}, [3]}, which scale poorly by exhaustively searching for discriminative subsequences [2]}.
The current best solution is to frame this problem as a Partially-Observe Markov Decision Process, where at each regularly-spaced timestep, a policy decides whether or not to stop and predict the label. Some halt RNNs early [10]}, [9]} while others use Deep Q-Networks [11]}.
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